Introduction

This notebook is to explore Canadian SARS-CoV-2 genomic and epidemiological data, for discussion with pillar 6’s team and for sharing with collaborators. These analyses can spur further research within or across pillars, be used for reports (or data dashboards), given to the science communication pillar for public dissemination, and the code can be repackaged to give to public health authorities for their internal use.

Canadian genomic and epidemiological data will be regularly pulled from various sources to keep these analyses up-to-date.

## 1. LOAD processed metadata of Canadian sequences (with latest pangolin, division, and full seq IDs)
#Download metadata from gisaid, put the date here:
gisaiddate="2021_12_10"
#date=2021_12_10
#tar -axf metadata_tsv_$date.tar.xz metadata.tsv -O | tr ' ' '_'  | sed 's/\t\t/\tNA\t/g' | sed 's/\t\t/\tNA\t/g' | sed 's/\t$/\tNA/g' | awk 'NR==1 || substr($1,9,6)=="Canada" && $8=="Human"' | sort -k3,3 > metadata_CANall_$date.uncorrected.csv 
#cat metadata_virrusseq_2021_12_08.tsv | tr ' ' '_'  | sed 's/\t\t/\tNA\t/g' | sed 's/\t\t/\tNA\t/g' | sed 's/\t$/\tNA/g' | awk 'NR!=1 && $43!="NA"' | cut -f5,43 | sort -k2,2 > epidatesfromvirrusseq
#join -1 3 metadata_CANall_2021_12_10.uncorrected.csv -a 1 -2 2 epidatesfromvirrusseq | awk '$4!=$23 && length($4)<10 && length($23)==10{$4=$23} {id=$1;$1=$2;$2=$3;$3=id} {print}' | tr ' ' '\t' > metadata_CANall_2021_12_10.csv
#zip CoVaRRNet_pillar6notebook/data_needed/metadata_CANall_$date.zip metadata_CANall_$date.csv

metaCANall <- read.csv(unz(paste("./data_needed/metadata_CANall_",gisaiddate,".zip",sep=""),paste("metadata_CANall_",gisaiddate,".csv",sep="")),sep="\t")
metaCANall$Collection_date <- as.Date(metaCANall$Collection_date)
#max(metaCANall$Collection.date) - min(metaCANall$Collection.date) #time diff: 580 days

#make a pango.group column

metaCANall$pango.group <- metaCANall$Variant
metaCANall$pango.group[is.na(metaCANall$pango.group)]<-"other"
metaCANall$pango.group[metaCANall$pango.group == "VOC_Alpha_202012/01_GRY_(B.1.1.7+Q.x)_first_detected_in_the_UK"]<-"Alpha"
metaCANall$pango.group[metaCANall$pango.group == "VOC_Beta_GH/501Y.V2_(B.1.351+B.1.351.2+B.1.351.3)_first_detected_in_South_Africa" ]<-"Beta"
metaCANall$pango.group[metaCANall$pango.group == "VOC_Gamma_GR/501Y.V3_(P.1+P.1.x)_first_detected_in_Brazil/Japan"   ]<-"Gamma"
metaCANall$pango.group[metaCANall$pango.group == "VOC_Delta_GK/478K.V1_(B.1.617.2+AY.x)_first_detected_in_India"   ]<-"Delta"
metaCANall$pango.group[metaCANall$Pango_lineage == "AY.25" ]<-"Delta AY.25"
metaCANall$pango.group[metaCANall$Pango_lineage == "AY.27" ]<-"Delta AY.27"
metaCANall$pango.group[metaCANall$pango.group == "VOI_Lambda_GR/452Q.V1_(C.37+C.37.1)_first_detected_in_Peru"  ]<-"Lambda"
metaCANall$pango.group[metaCANall$pango.group == "VOI_Mu_GH_(B.1.621+B.1.621.1)_first_detected_in_Colombia" ]<-"Mu"
metaCANall$pango.group[metaCANall$pango.group == "VOI_Mu_GH_(B.1.621+B.1.621.1)_first_detected_in_Colombia" ]<-"Mu"
metaCANall$pango.group[str_detect(metaCANall$pango.group,"V") ]<-"other"
metaCANall$pango.group[metaCANall$Pango_lineage == "A.23.1" ]<-"A.23.1"
metaCANall$pango.group[metaCANall$Pango_lineage == "B.1.438.1" ]<-"B.1.438.1"

## 2. LOAD epidemiological data (PHAC)



epidataCANall <- read.csv(url("https://health-infobase.canada.ca/src/data/covidLive/covid19-download.csv"))
#from: https://health-infobase.canada.ca/covid-19/epidemiological-summary-covid-19-cases.html?stat=num&measure=total&map=pt#a2
epidate = tail(epidataCANall,1)$date
#epidataAlberta <- epidataCANall[ which(epidataCANall$prnameFR=='Alberta'), ]
#print(epidataAlberta$numtoday)

Snapshot: SARS-CoV-2 in Canada

Variants in Canada

Sequence counts for all Canadian data in GISAID, up to 2021-12-14, shows that by end of summer Alpha and Gamma were still the most sequenced variants. Note that some of the “other” category include some Delta sublineages (AYs) but overall, from the beginning of the pandemic to the fall of 2021, Canadian sequences were mostly of the wildtype lineages (pre-VOCs).

# --- Histogram plot: counts per variant of all canadian data -------------


listpango <- unique(metaCANall$pango.group)
print(listpango)

##  [1] "other"       "Alpha"       "B.1.438.1"   "Beta"        "A.23.1"     
##  [6] "Gamma"       "Delta"       "Lambda"      "Delta AY.27" "Delta AY.25"
## [11] "Mu"

listpango <- listpango[order(listpango)]
getpal <- colorRampPalette(brewer.pal(9, "Paired")) #"Set3
pal <- getpal(length(listpango))
names(pal) <- listpango


#------------- counts over time (by week)

plot_metadatadf <- function(meta_tab,pal) {
  meta_tab <- filter(meta_tab, !is.na(meta_tab$Collection_date))
  df1 <- meta_tab %>% group_by(pango.group) %>% group_by(week = cut(Collection_date, "week")) #adds week coloum
  dfcount <- df1 %>% group_by(week) %>% count(pango.group)
  #plot
  ggplot(dfcount, aes(x=as.Date(week), y= n, fill=pango.group))+geom_bar(stat = "identity")+
    scale_fill_manual(breaks=listpango, values = pal)+ ylab("")+xlab("")+
    scale_x_date(date_breaks = "28 day")+
    theme_classic()+theme(axis.text.x = element_text(angle=90, vjust=0.1,hjust=0.1))
  
}
  
plot_metadatadf_freq <- function(meta_tab,pal) {
  meta_tab <- filter(meta_tab, !is.na(meta_tab$Collection_date))
  df1 <- meta_tab %>% group_by(pango.group) %>% group_by(week = cut(Collection_date, "week")) #adds week coloum
  dfcount <- df1 %>% group_by(week) %>% count(pango.group)
  dfcount %>% mutate(freq = prop.table(n)) -> dfprop
  
  #plot
  ggplot(dfprop, aes(x= as.Date(week), y=freq, fill=pango.group))+geom_bar(stat = "identity")+
    scale_fill_manual(breaks=listpango, values = pal)+ ylab("")+xlab("")+
    scale_x_date(date_breaks = "28 day")+
    theme_classic()+theme(axis.text.x = element_text(angle=90, vjust=0.1,hjust=0.1))
}


plot_metadatadf(metaCANall,pal)

plot_metadatadf_freq(metaCANall,pal)

There are two PANGO lineages that have a Canadian origin and have predominately spread within Canada (with some exportations internationally): B.1.438.1 and B.1.1.176.

Other lineages of Canadian interest:

A.2.5.2 - an A lineage (clade 19B) that spread in Quebec, involved in several outbreaks, before Delta arrived
B.1.2 - an American (USA) lineage that spread well in Canada
B.1.160 - an European lineages that spread well in Canada

Provinces

Here we show the same plots but for each provinces

loc=sapply(strsplit(metaCANall$Location,"_/_"), `[`, 3)
metaCANall$province <- loc
print(unique(metaCANall$province))

##  [1] "New_Brunswick"             "Manitoba"                 
##  [3] "Nova_Scotia"               "Newfoundland_and_Labrador"
##  [5] "Ontario"                   "British_Columbia"         
##  [7] "Quebec"                    "Alberta"                  
##  [9] NA                          "Saskatchewan"             
## [11] "Newfoundland"

British_Columbia

plot_metadatadf(metaCANall[ which(metaCANall$province=='British_Columbia'), ],pal)

plot_metadatadf_freq(metaCANall[ which(metaCANall$province=='British_Columbia'), ],pal)

Alberta

plot_metadatadf(metaCANall[ which(metaCANall$province=='Alberta'), ],pal)

plot_metadatadf_freq(metaCANall[ which(metaCANall$province=='Alberta'), ],pal)

Saskatchewan

plot_metadatadf(metaCANall[ which(metaCANall$province=='Saskatchewan'), ],pal)

plot_metadatadf_freq(metaCANall[ which(metaCANall$province=='Saskatchewan'), ],pal)

Manitoba

plot_metadatadf(metaCANall[ which(metaCANall$province=='Manitoba'), ],pal)

plot_metadatadf_freq(metaCANall[ which(metaCANall$province=='Manitoba'), ],pal)

Ontario

plot_metadatadf(metaCANall[ which(metaCANall$province=='Ontario'), ],pal)

plot_metadatadf_freq(metaCANall[ which(metaCANall$province=='Ontario'), ],pal)

Quebec

plot_metadatadf(metaCANall[ which(metaCANall$province=='Quebec'), ],pal)

plot_metadatadf_freq(metaCANall[ which(metaCANall$province=='Quebec'), ],pal)

Nova_Scotia

plot_metadatadf(metaCANall[ which(metaCANall$province=='Nova_Scotia'), ],pal)

plot_metadatadf_freq(metaCANall[ which(metaCANall$province=='Nova_Scotia'), ],pal)

New_Brunswick

plot_metadatadf(metaCANall[ which(metaCANall$province=='New_Brunswick'), ],pal)

plot_metadatadf_freq(metaCANall[ which(metaCANall$province=='New_Brunswick'), ],pal)

Newfoundland_and_Labrador

subtab=metaCANall[ which(metaCANall$province=='Newfoundland' | metaCANall$province=='Newfoundland_and_Labrador' ), ]
plot_metadatadf(subtab,pal)

plot_metadatadf_freq(subtab,pal)

## Canadian trees

Down-sampled Canadian SARS-CoV-2 genomes. Taken from GISAID Sept. 12th, 2021. Alignment GISAID, ML tree in IQ-tree. This tree was generated using TreeTime and visualized in ggtree.

This tree shows several features of VOC spread in Canada:

Alpha spread across most of Canada
Gamma was mostly located in BC
Alberta represents most of the Delta cases
Expansion of Alpha and Gamma coincide with a decrease in detection of wildtype variants
Delta is the bulk of what was being sequenced at the end of the summer

These last two points are suggestive of strain (variant) replacement, i.e. competitive exclusion, but more detailed analyses would be required to clarify this.

Divergence tree from TreeTime run, visualized in ggtree.

Evolution and growth rates of SARS-CoV-2 in Canada

There are various methods to investigate changes in evolutionary rates of VOC/VOIs and compare their relative fitness in an epidemiological context.

For example, root-to-tip plots give estimates of substitution rates. Clusters with stronger positive slopes than the average SARS-CoV-2 dataset, are an indication that they are accumlating mutations at a faster pace, or clusters that jump up above the average could inidcate a mutational jump (simlar to what we saw with Alpha when it first appeared in the UK).

ML IQ-tree run in Tempest for root-to-tip data. Linear regression and plotting in R.

Overall, the evolutionary rate of the Canadian data (0.000875 subs/site/year, grey line) is very close to the global average of 24.162 subs/year (= 0.000822 subs/site/year). Delta, Gamma, and Alpha are all slower than this global average.

Add here:

Root-to-tip plot(s)
growth rates (Sally’s code)
dN/dS (by variant and by gene/domains)
Tajima’s D
other? (e.g. PyR0, nnet R, ?)
Delta and its sublineages (specific plots as above, also delta w/+w/o E484K in Canada/PTs)

Are there any BEAST analyses we’d like to do? e.g. infer R0, serial interval, etc for the different Delta sublineages (might have to restrict this geographically to specific PTs with enough coverage)

List of useful tools

Collect a list of bioinformatics, phylogenetic, and modelling tools that are useful for SARS-CoV-2 analyses:

UShER: Ultrafast Sample placement on Existing tRee - for placing a small-ish dataset into the global GISAID phylogenetic tree [web-version: https://genome.ucsc.edu/cgi-bin/hgPhyloPlace, local-version: https://shusher.gi.ucsc.edu/]
iTol: Tree visualization tool [https://itol.embl.de/itol.cgi]
List of (mostly) modelling tools by CANMOD: [https://canmod.net/tools], includes RECON, outbreak tools for both modelling and genomic epi [https://github.com/reconhub]
List of homoplaises in SARS-CoV-2: https://github.com/corneliusroemer/ncov-simplest/blob/main/data/exclude_sites_light.txt
Erin Gill’s COVID-19 dashboard [https://github.com/eringill/COVID_dashboard_reboot]
The Epi Graph Network: training platform. Programming tools for health data analysis, African/European network of researchers and WHO Afro. [https://thegraphnetwork.training/]
Paul’s subsampling tool of SARS-CoV-2 genome ensembles [https://github.com/nodrogluap/nybbler]
Pokay tool for checking and reporitng mismatches [https://github.com/nodrogluap/pokay]
IRIDA Canada’s ID analysis platform for genomic epi [https://github.com/pvanheus/irida]
cov-lineages (summaries of PANGO lineages) [https://cov-lineages.org/lineage_list.html]

Data sources

GISAID
VirusSeq Portal
PHAC
various PT PH websites (e.g. INSPQ)

Session info

The version numbers of all packages in the current environment as well as information about the R install is reported below.

Hide

Show

sessionInfo()

## R version 4.1.2 (2021-11-01)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.3 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] viridis_0.6.2      viridisLite_0.4.0  colorspace_2.0-2   RColorBrewer_1.1-2
##  [5] kableExtra_1.3.4   gridExtra_2.3      ggbeeswarm_0.6.0   DT_0.20           
##  [9] cowplot_1.1.1      ggtree_3.2.1       phytools_0.7-90    maps_3.4.0        
## [13] phangorn_2.8.0     tidytree_0.3.6     phylotools_0.2.2   ape_5.5           
## [17] treeio_1.18.1      lubridate_1.8.0    reticulate_1.22    knitr_1.36        
## [21] forcats_0.5.1      stringr_1.4.0      dplyr_1.0.7        purrr_0.3.4       
## [25] readr_2.1.1        tidyr_1.1.4        tibble_3.1.6       ggplot2_3.3.5     
## [29] tidyverse_1.3.1   
## 
## loaded via a namespace (and not attached):
##  [1] ellipsis_0.3.2          fs_1.5.2                aplot_0.1.1            
##  [4] rstudioapi_0.13         farver_2.1.0            fansi_0.5.0            
##  [7] xml2_1.3.3              codetools_0.2-18        mnormt_2.0.2           
## [10] jsonlite_1.7.2          broom_0.7.10            dbplyr_2.1.1           
## [13] png_0.1-7               compiler_4.1.2          httr_1.4.2             
## [16] backports_1.4.0         assertthat_0.2.1        Matrix_1.4-0           
## [19] fastmap_1.1.0           lazyeval_0.2.2          cli_3.1.0              
## [22] htmltools_0.5.2         tools_4.1.2             igraph_1.2.9           
## [25] coda_0.19-4             gtable_0.3.0            glue_1.5.1             
## [28] clusterGeneration_1.3.7 fastmatch_1.1-3         Rcpp_1.0.7             
## [31] cellranger_1.1.0        jquerylib_0.1.4         vctrs_0.3.8            
## [34] svglite_2.0.0           nlme_3.1-153            xfun_0.28              
## [37] rvest_1.0.2             lifecycle_1.0.1         MASS_7.3-54            
## [40] scales_1.1.1            hms_1.1.1               parallel_4.1.2         
## [43] expm_0.999-6            yaml_2.2.1              ggfun_0.0.4            
## [46] yulab.utils_0.0.4       stringi_1.7.6           highr_0.9              
## [49] plotrix_3.8-2           rlang_0.4.12            pkgconfig_2.0.3        
## [52] systemfonts_1.0.3       evaluate_0.14           lattice_0.20-45        
## [55] labeling_0.4.2          patchwork_1.1.1         htmlwidgets_1.5.4      
## [58] tidyselect_1.1.1        magrittr_2.0.1          R6_2.5.1               
## [61] generics_0.1.1          combinat_0.0-8          DBI_1.1.1              
## [64] pillar_1.6.4            haven_2.4.3             withr_2.4.3            
## [67] scatterplot3d_0.3-41    modelr_0.1.8            crayon_1.4.2           
## [70] utf8_1.2.2              tmvnsim_1.0-2           tzdb_0.2.0             
## [73] rmarkdown_2.11          grid_4.1.2              readxl_1.3.1           
## [76] reprex_2.0.1            digest_0.6.29           webshot_0.5.2          
## [79] numDeriv_2016.8-1.1     gridGraphics_0.5-1      munsell_0.5.0          
## [82] beeswarm_0.4.0          ggplotify_0.1.0         vipor_0.4.5            
## [85] quadprog_1.5-8

CoVaRR-Net Pillar 6

R Notebook: Genomic epidemiology analyses

Carmen Lia Murall, Raphael Poujol, Susanne Kraemer

December 15, 2021

Introduction

Snapshot: SARS-CoV-2 in Canada

Variants in Canada

Provinces

British_Columbia

Alberta

Saskatchewan

Manitoba

Ontario

Quebec

Nova_Scotia

New_Brunswick

Newfoundland_and_Labrador

Evolution and growth rates of SARS-CoV-2 in Canada

List of useful tools

Data sources

Session info

Hide

Show

CoVaRR-Net Pillar 6

R Notebook: Genomic epidemiology analyses

Carmen Lia Murall, Raphael Poujol, Susanne Kraemer

December 15, 2021

Introduction

Snapshot: SARS-CoV-2 in Canada

Sequencing coverage and sharing

Variants in Canada

Provinces

British_Columbia

Alberta

Saskatchewan

Manitoba

Ontario

Quebec

Nova_Scotia

New_Brunswick

Newfoundland_and_Labrador

Evolution and growth rates of SARS-CoV-2 in Canada

List of useful tools

Data sources

Session info

Hide

Show